GCN, or Graph Convolutional Network, is a type of neural network designed to work with graph-structured data. Unlike traditional neural networks that operate on grid-like data, GCNs can process data represented as nodes and edges, making them suitable for tasks like social network analysis, recommendation systems, and molecular chemistry. The key feature of GCNs is their ability to learn from the local structure of the graph. By aggregating information from neighboring nodes, GCNs can capture complex relationships and patterns, enabling them to make predictions or classifications based on the graph's topology and features.